Chaos for optimization

Abstract

Optimization is the process to find the optimal solution from possible solutions. However, in many practical optimization problems, the number of feasible solutions has exploded with the size of the problem, which makes it impossible to obtain the global optimal solution. Chaos is a kind of random-like movement generated from a nonlinear system. Many researchers demonstrate that a series of nonlinear characteristics of chaos, ergodicity, non-periodic, randomness, etc., can improve the randomness and speed when searching optimal solutions within a short time. This dissertation presents a state-of-the-art review of chaos for optimization. The related works are reviewed from chaotic approaches and optimization applications. Then simulation experiments of several chaos optimization algorithms are given in detail. Finally, the superiority of chaos and two factors affecting performance are discussed, as well as some future research directions are provided.